Exercise 1

Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts/data

Step 2. Download the dataset to your computer and unzip it.

Step 3. Use the tsv file and assign it to a dataframe called food

In [18]:

import pandas as pd
import numpy as np
food = pd.read_csv('en.openfoodfacts.org.products.tsv',sep='\t')

C:\Users\shuos\AppData\Local\Temp\ipykernel_25344\1762735028.py:3: DtypeWarning: Columns (0,3,5,19,20,24,25,26,27,28,36,37,38,39,48) have mixed types. Specify dtype option on import or set low_memory=False.
  food = pd.read_csv('en.openfoodfacts.org.products.tsv',sep='\t')

Step 4. See the first 5 entries

In [19]:

food.head()

	code	url	creator	created_t	created_datetime	last_modified_t	last_modified_datetime	product_name	generic_name	quantity	...	fruits-vegetables-nuts_100g	fruits-vegetables-nuts-estimate_100g	collagen-meat-protein-ratio_100g	cocoa_100g	chlorophyl_100g	carbon-footprint_100g	nutrition-score-fr_100g	nutrition-score-uk_100g	glycemic-index_100g	water-hardness_100g
0	3087	http://world-en.openfoodfacts.org/product/0000...	openfoodfacts-contributors	1474103866	2016-09-17T09:17:46Z	1474103893	2016-09-17T09:18:13Z	Farine de blé noir	NaN	1kg	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	4530	http://world-en.openfoodfacts.org/product/0000...	usda-ndb-import	1489069957	2017-03-09T14:32:37Z	1489069957	2017-03-09T14:32:37Z	Banana Chips Sweetened (Whole)	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	14.0	14.0	NaN	NaN
2	4559	http://world-en.openfoodfacts.org/product/0000...	usda-ndb-import	1489069957	2017-03-09T14:32:37Z	1489069957	2017-03-09T14:32:37Z	Peanuts	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.0	NaN	NaN
3	16087	http://world-en.openfoodfacts.org/product/0000...	usda-ndb-import	1489055731	2017-03-09T10:35:31Z	1489055731	2017-03-09T10:35:31Z	Organic Salted Nut Mix	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	12.0	12.0	NaN	NaN
4	16094	http://world-en.openfoodfacts.org/product/0000...	usda-ndb-import	1489055653	2017-03-09T10:34:13Z	1489055653	2017-03-09T10:34:13Z	Organic Polenta	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 163 columns

Step 5. What is the number of observations in the dataset?

In [21]:

food.shape[0]

Step 6. What is the number of columns in the dataset?

In [22]:

food.shape[1]
food.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 356027 entries, 0 to 356026
Columns: 163 entries, code to water-hardness_100g
dtypes: float64(107), object(56)
memory usage: 442.8+ MB

Step 7. Print the name of all the columns.

In [23]:

food.columns

Index(['code', 'url', 'creator', 'created_t', 'created_datetime',
       'last_modified_t', 'last_modified_datetime', 'product_name',
       'generic_name', 'quantity',
       ...
       'fruits-vegetables-nuts_100g', 'fruits-vegetables-nuts-estimate_100g',
       'collagen-meat-protein-ratio_100g', 'cocoa_100g', 'chlorophyl_100g',
       'carbon-footprint_100g', 'nutrition-score-fr_100g',
       'nutrition-score-uk_100g', 'glycemic-index_100g',
       'water-hardness_100g'],
      dtype='object', length=163)

Step 8. What is the name of 105th column?

In [24]:

food.columns[104]

'-glucose_100g'

Step 9. What is the type of the observations of the 105th column?

In [25]:

food.dtypes[food.columns[104]]

dtype('float64')

Step 10. How is the dataset indexed?

In [26]:

food.index

RangeIndex(start=0, stop=356027, step=1)

Step 11. What is the product name of the 19th observation?

In [27]:

food.values[18][7]

'Lotus Organic Brown Jasmine Rice'